What Makes TIBCO Enterprise Runtime for R (TERR) Unique?

In recent years, millions of statisticians and data scientists have flocked to using the R language for computational statistics, visualization, and data science. While there are a number of adaptations of the open source R language, it’s important to recognize that not all R tools are alike.

While one approach to R is to modify and extend the open source R engine, TIBCO Enterprise Runtime for R is unique in that it is the only commercially-developed alternative R interpreter.

We developed TERR from the ground up, leveraging our 20-plus years of experience with the closely-related S-PLUS engine, and all of our amassed experience in architecting, extending, and maintaining the S-PLUS engine now carries over to the R community.

With this experience in hand, TERR has been architected to be faster than the open source R engine. In fact, based on benchmark tests, TERR is roughly 2x to 10x faster than open source R when applied to small data sets. It’s even faster when applied to large data sets – 10x to 100x faster.

We also designed TERR to be more scalable and handle memory more efficiently than the open source R engine. One of R’s chief limitations is that many R functions can quickly consume available memory when applied to bigger data, slowing down performance out of proportion to data size. TERR’s efficiencies in memory management means that it is more linear in performance as data sizes increase.

Another important distinction is that TERR can be licensed to clients and business partners for tight integration with their own software.

Other platforms typically integrate loosely with open source R, mainly to protect their IP from the risk of contamination with R’s GPL (General Public License) license. Tighter integration with TERR results in an improved experience for clients and their customers since they don’t have to download, install, and configure R separately.

As a commercially-developed alternative R interpreter, TIBCO is able to provide full support for the TERR engine. This provides enterprise customers the confidence to use it in their own production environments. Many companies that use open source R for development are reluctant to use it for production without the stamp of confidence that the support of a large organization such as TIBCO provides.

The agility to quickly deploy R analytics into production benefits end users. TERR users can create and test models directly in RStudio and then distribute them to a wide community of users via Spotfire. This approach eliminates the delays of reimplementing the analytics in another environment, providing decision-makers with direct access to tests, models, and statistical analyses.

Next Steps:

For more insights from the Briefing Room, we invite you to view the full recording on demand: “Analytic Maturity: Bringing Predictive into the Mainstream.”

Subscribe to our blog to stay up to date on the latest insights and trends in big data and big data analytics.